Appendix - achyut3598/SmartHackSmasher GitHub Wiki
Repository
Our repository, including code and creations, can be found here: https://github.com/achyut3598/SmartHackSmasher
Meeting History
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Meetings with Project Advisor
- 08/31/2020 900-1000 - General brainstorming meetings for ideas and direction.
- 09/14/2020 1000-1100 - Discussion of research into machine learning, car automation, and security.
- 10/02/2020 1000-1100 - Discussion of algorithm research and datasets.
- 10/16/2020 1000-1100 - Selection of algorithms and ideas on implementation.
- 11/16/2020 1000-1100 - Progress updates, discussion of roadblocks and solutions.
- 11/24/2020 1000-1100 - Presentation of code and implementation of algorithms of traffic dataset.
- 12/01/2020 1000-1100 - Wrap-up presentation of work done/project during semester.
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Meetings with Group Members
- 08/29/2020 1300-1550 - General brainstorming and research.
- 09/05/2020 1400-1445 - Research and discussion of papers.
- 09/11/2020 1100-1215 - Discussion of papers. Preliminary dataset exploration. (Specifically google heatmap)
- 09/12/2020 1400-1500 - Dataset experimentation and research. (Specifically Waymo open dataset)
- 09/20/2020 1100-1320 - Finalization of datasets and initial discussion of algorithms.
- 09/23/2020 1330-1440 - Algorithm/dataset experimentation.
- 09/27/2020 1100-1200 - Algorithm/dataset experimentation.
- 10/9/2020 1100-1130 - Algorithm/dataset experimentation.
- 10/12/2020 1800-1900 - Algorithm/dataset experimentation.
- 10/25/2020 1100-1215 - Initial research into presentation format/simulation, possible alternatives including Unity.
- 11/01/2020 1100-1200 - Algorithm/dataset experimentation.
- 11/08/2020 1130-1215 - Algorithm/dataset experimentation.
- 11/21/2020 1100-1200 - Presentation preparation and discussion.
- 11/28/2020 1100-1130 - Algorithm/dataset experimentation, review, ideas for moving forward/December work.
Citations/References
- https://search-proquest-com.proxy.libraries.uc.edu/docview/2168028241?pq-origsite=summon
- https://ieeexplore-ieee-org.proxy.libraries.uc.edu/document/8903691
- https://ieeexplore-ieee-org.proxy.libraries.uc.edu/document/7917080
- https://doaj-org.proxy.libraries.uc.edu/article/7ae8556d95f84e238fda0eea7843e9d6
- https://www-sciencedirect-com.proxy.libraries.uc.edu/science/article/pii/S2214209620300371?via%3Dihub
- https://www-sciencedirect-com.proxy.libraries.uc.edu/science/article/pii/S0045790620305723?via%3Dihub
- https://www.researchgate.net/publication/331250940_Integrity_verification_of_Docker_containers_for_a_lightweight_cloud_environment
- https://waymo.com/open
- https://boxy-dataset.com/boxy/
- https://unsupervised-llamas.com/llamas/
- https://hci.iwr.uni-heidelberg.de/content/bosch-small-traffic-lights-dataset
- https://diyrobocars.com/diy-robocars-oakland-warehouse-datasets/
- http://www.cvl.isy.liu.se/en/research/datasets/amuse/
- https://www.kaggle.com/meowmeowmeowmeowmeow/gtsrb-german-traffic-sign
- https://iopscience.iop.org/article/10.1149/1945-7111/ab67a8
- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6983166/
- https://towardsdatascience.com/the-mostly-complete-chart-of-neural-networks-explained-3fb6f2367464
- https://medium.com/towards-artificial-intelligence/main-types-of-neural-networks-and-its-applications-tutorial-734480d7ec8e
- https://www.mdpi.com/journal/sensors/special_issues/neural-networks-and-sensors
- https://www.microsoft.com/en-us/download/details.aspx?id=52367&from=https%3A%2F%2Fresearch.microsoft.com%2Fen-us%2Fdownloads%2Fb16d359d-d164-469e-9fd4-daa38f2b2e13%2F
- https://www.microsoft.com/en-us/research/publication/t-drive-trajectory-data-sample/?from=https%3A%2F%2Fresearch.microsoft.com%2Fapps%2Fpubs%2F%3Fid%3D152883
- https://towardsdatascience.com/understanding-variational-autoencoders-vaes-f70510919f73 https://www.ijcai.org/Proceedings/2017/0222.pdf
- https://github.com/tensorflow/gan
- https://stackabuse.com/image-recognition-in-python-with-tensorflow-and-keras/
- https://www.kdnuggets.com/2019/08/introduction-image-segmentation-k-means-clustering.html
- https://github.com/IBM/powerai-counting-cars/tree/master/data